• Title/Summary/Keyword: artificial intelligence-based model

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Fake News Detection Using Deep Learning

  • Lee, Dong-Ho;Kim, Yu-Ri;Kim, Hyeong-Jun;Park, Seung-Myun;Yang, Yu-Jun
    • Journal of Information Processing Systems
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    • v.15 no.5
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    • pp.1119-1130
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    • 2019
  • With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

Korean Sentiment Analysis Using Natural Network: Based on IKEA Review Data

  • Sim, YuJeong;Yun, Dai Yeol;Hwang, Chi-gon;Moon, Seok-Jae
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.173-178
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    • 2021
  • In this paper, we find a suitable methodology for Korean Sentiment Analysis through a comparative experiment in which methods of embedding and natural network models are learned at the highest accuracy and fastest speed. The embedding method compares word embeddeding and Word2Vec. The model compares and experiments representative neural network models CNN, RNN, LSTM, GRU, Bi-LSTM and Bi-GRU with IKEA review data. Experiments show that Word2Vec and BiGRU had the highest accuracy and second fastest speed with 94.23% accuracy and 42.30 seconds speed. Word2Vec and GRU were found to have the third highest accuracy and fastest speed with 92.53% accuracy and 26.75 seconds speed.

Target and Swear Word Detection Using Sentence Analysis in Real-Time Chatting (실시간 채팅 환경에서 문장 분석을 이용한 대상자 및 비속어 검출)

  • Yeom, Choongseok;Jang, Junyoung;Jang, Yuhwan;Kim, Hyun-chul;Park, Heemin
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.1
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    • pp.83-87
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    • 2021
  • By the increase of internet usage, communicating online became an everyday thing. Thereby various people have experienced profanity by anonymous users. Nowadays lots of studies tried to solve this problem using artificial intelligence, but most of the solutions were for non-real time situations. In this paper, we propose a Telegram plugin that detects swear words using word2vec, and an algorithm to find the target of the sentence. We vectorized the input sentence to find connections with other similar words, then inputted the value to the pre-trained CNN (Convolutional Neural Network) model to detect any swears. For target recognition we proposed a sequential algorithm based on KoNLPY.

System simulation and synchronization for optimal evolutionary design of nonlinear controlled systems

  • Chen, C.Y.J.;Kuo, D.;Hsieh, Chia-Yen;Chen, Tim
    • Smart Structures and Systems
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    • v.26 no.6
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    • pp.797-807
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    • 2020
  • Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.

Toward Sentiment Analysis Based on Deep Learning with Keyword Detection in a Financial Report (재무 보고서의 키워드 검출 기반 딥러닝 감성분석 기법)

  • Jo, Dongsik;Kim, Daewhan;Shin, Yoojin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.5
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    • pp.670-673
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    • 2020
  • Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

A Survey on Feature Store (Feature 저장소 기술 동향)

  • Hur, S.J.;Kim, J.Y.
    • Electronics and Telecommunications Trends
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    • v.36 no.2
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    • pp.65-74
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    • 2021
  • In this paper, we discussed the necessity and importance of introducing feature stores to establish a collaborative environment between data engineering work and data science work. We examined the technology trends of feature stores by analyzing the status of some major feature stores. Moreover, by introducing a feature store, we can reduce the cost of performing artificial intelligence (AI) projects and improve the performance and reliability of AI models and the convenience of model operation. The future task is to establish technical requirements for establishing a collaborative environment between data engineering work and data science work and develop a solution for providing a collaborative environment based on this.

Generating and Validating Synthetic Training Data for Predicting Bankruptcy of Individual Businesses

  • Hong, Dong-Suk;Baik, Cheol
    • Journal of information and communication convergence engineering
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    • v.19 no.4
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    • pp.228-233
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    • 2021
  • In this study, we analyze the credit information (loan, delinquency information, etc.) of individual business owners to generate voluminous training data to establish a bankruptcy prediction model through a partial synthetic training technique. Furthermore, we evaluate the prediction performance of the newly generated data compared to the actual data. When using conditional tabular generative adversarial networks (CTGAN)-based training data generated by the experimental results (a logistic regression task), the recall is improved by 1.75 times compared to that obtained using the actual data. The probability that both the actual and generated data are sampled over an identical distribution is verified to be much higher than 80%. Providing artificial intelligence training data through data synthesis in the fields of credit rating and default risk prediction of individual businesses, which have not been relatively active in research, promotes further in-depth research efforts focused on utilizing such methods.

Proposal of Electronic Engineering Exploration Learning Operation Using Computing Thinking Ability

  • LEE, Seung-Woo;LEE, Sangwon
    • International Journal of Advanced Culture Technology
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    • v.9 no.4
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    • pp.110-117
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    • 2021
  • The purpose of the study is to develop effective teaching methods to strengthen the major learning capabilities of electronic engineering learners through inquiry learning using computing thinking ability. To this end, first, in the electronic engineering curriculum, we performed teaching-learning through an inquiry and learning model related to mathematics, probability, and statistics under the theme of various majors in electronic engineering, focusing on understanding computing thinking skills. Second, an efficient electronic engineering subject inquiry class operation using computing thinking ability was conducted, and electronic engineering-linked education contents based on the components of computer thinking were presented. Third, by conducting a case study on inquiry-style teaching using computing thinking skills in the electronic engineering curriculum, we identified the validity of the teaching method to strengthen major competency. In order to prepare for the 4th Industrial Revolution, by implementing mathematics, probability, statistics-related linkage, and convergence education to foster convergent talent, we tried to present effective electronic engineering major competency enhancement measures and cope with innovative technological changes.

Generative Adversarial Network based Mobility Prediction Model in Wireless Network (무선 네트워크 환경에서의 생성적 적대 신경망 기반 이동성 예측 모델)

  • Jang, Boyun;Raza, Syed Muhammad;Kim, Moonseong;Choo, Hyunseung
    • Annual Conference of KIPS
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    • 2020.11a
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    • pp.168-171
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    • 2020
  • 초저지연성을 요구하는 5G 네트워크 환경에서 기기의 핸드오버를 능동적으로 조절하는 시스템의 중요성이 대두되고 있으며, 특히 핸드오버 시 기기의 이동성을 예측하는 것은 필수적이다. 딥러닝 모델의 일종인 생성적 적대 신경망은 두 신경망 사이의 경쟁 구도를 이용하여 두 신경망의 성능을 모두 높이는 목적으로 사용된다. 본 논문에서는 주로 데이터 생성 모델로 사용되는 생성적 적대 신경망을 이용하여 무선 네트워크 환경에서 기기의 이동성을 예측하는 시스템을 개발하였다. 이를 통해 실제 모바일 네트워크 환경에 적용되었을 경우 핸드오버 속도를 높이도록 한다.

A Study on the Improvement of Tesseract-based OCR Model Recognition Rate using Ontology (온톨로지를 이용한 tesseract 기반의 OCR 모델 인식률 향상에 관한 연구)

  • Hwang, Chi-gon;Yun, Dai Yeol;Yoon, Chang-Pyo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.438-440
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    • 2021
  • With the development of machine learning, artificial intelligence techniques are being applied in various fields. Among these fields, there is an OCR technique that converts characters in images into text. The tesseract developed by HP is one of those techniques. However, the recognition rate for recognizing characters in images is still low. To this end, we try to improve the conversion rate of the text of the image through the post-processing process that recognizes the context using the ontology.

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